Detection of abrupt changes in autonomous system fault analysis using spatial adaptive estimation of nonparametric regression

The paper deals with the detection of abrupt changes in autonomous systems. We consider this problem in the presence of Gaussian noise and solve it in two steps. At first, spatial adaptive estimation of nonparametric regression is used to estimate the observable data. Then Filtered Derivative Algori...

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Main Authors: Kalmuk, Alexander, Granichin, Oleg, Granichina, Olga, Mingyue Ding
Format: Conference Proceeding
Language:English
Subjects:
Accelerometers
Adaptation models
Adaptive algorithms
Adaptive estimation
Conferences
Derivatives
Electronics
Estimation
Faults
Filtering algorithms
Gaussian
Probability density function
Regression
Thresholds
Vehicles
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Granichin, Oleg
Granichina, Olga
Mingyue Ding
description The paper deals with the detection of abrupt changes in autonomous systems. We consider this problem in the presence of Gaussian noise and solve it in two steps. At first, spatial adaptive estimation of nonparametric regression is used to estimate the observable data. Then Filtered Derivative Algorithm is used to detect abrupt changes in the obtained data using an adaptive threshold. The estimation of this adaptive threshold is presented. This approach is then applied to demonstrate the slowdown detection of a small autonomous vehicle.
doi_str_mv 10.1109/ACC.2016.7526749
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subjects Accelerometers
Adaptation models
Adaptive algorithms
Adaptive estimation
Conferences
Derivatives
Electronics
Estimation
Faults
Filtering algorithms
Gaussian
Probability density function
Regression
Thresholds
Vehicles
title Detection of abrupt changes in autonomous system fault analysis using spatial adaptive estimation of nonparametric regression
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